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17th international conference and workshops on algorithms and computation, WALCOM 2023

17th International Conference and Workshops on Algorithms an...

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17th international conference and workshops on algorithms and computation, WALCOM 2023

ISBN: (纸本)9783031270505

the proceedings contain 32 papers. the special focus in this conference is on algorithms and computation. the topics include: Internal Longest Palindrome Queries in Optimal Time;finding the Cyclic Covers of a String;efficient Non-isomorphic Graph Enumeration algorithms for Subclasses of Perfect Graphs;better Hardness Results for the Minimum Spanning Tree Congestion Problem;energy Efficient Sorting, Selection and Searching;reconfiguration of Vertex-Disjoint Shortest Paths on Graphs;k-Transmitter Watchman Routes;splitting Plane Graphs to Outerplanarity;the Family of Fan-Planar Graphs;certifying Induced Subgraphs in Large Graphs;some Algorithmic Results for Eternal Vertex Cover Problem in Graphs;on the Complexity of Distance-d Independent Set Reconfiguration;on Star-Multi-interval Pairwise Compatibility Graphs;parameterized Complexity of Optimizing List Vertex-Coloring through Reconfiguration;parameterized Complexity of Path Set Packing;interweaving Real-Time Jobs with Energy Harvesting to Maximize throughput;recognizing When a Preference System is Close to Admitting a Master List;groups Burning: Analyzing Spreading Processes in Community-Based Networks;roman k-Domination: Hardness, Approximation and Parameterized Results;minimum Ply Covering of Points with Unit Squares;on the Parameterized Complexity of Compact Set Packing;structural Parameterization of Cluster Deletion;parity Permutation Pattern Matching;overlapping Edge Unfoldings for Archimedean Solids and (Anti)prisms;flipping Plane Spanning Paths;away from Each Other;piercing Diametral Disks Induced by Edges of Maximum Spanning Trees;reflective Guarding a Gallery;improved and Generalized algorithms for Burning a Planar Point Set;preface.

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algorithms for computation of Topological Measures Based on Binary Decision Diagrams 17

Algorithms for Computation of Topological Measures Based on ...

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17th IEEE international Scientific conference on Informatics, INFORMATICS 2024

作者： Mrena, Michal Rusnak, Patrik Kvassay, Miroslav University of Zilina Department of Informatics Zilina Slovakia

ISBN: (纸本)9798350387674

Reliability analysis is an important part of investigating a system and its components. In reliability analysis, it is necessary to select an appropriate representation for the system and a description of its operation. One representation is a binary-state system. A structure function can be used in describing the operation of the system. One part of reliability analysis is topological analysis. Several metrics are used in topological analysis;one of them is the system state frequency. For its calculation, it is necessary to choose a suitable representation of the structure function, especially if its computation is done on a computer. the binary decision diagram is one such representation. there are currently two algorithms for computing this metric, but they have certain limitations. therefore, a new algorithm is presented in this paper that addresses the limitations of these previous algorithms. this new algorithm is also tested with experimental validation. © 2024 IEEE.

关键词： algorithm binary decision diagram system state frequency topological analysis

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Enhanced Multi-Bit computation Using CIM SRAM Technology 17

Enhanced Multi-Bit Computation Using CIM SRAM Technology

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17th IEEE international conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Zhao, Ruiyong Hu, Yibo Ren, Zhipeng Yin, Yizhe Chen, Jing Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9798350361834

Tackling the challenges of substantial data loading latency and excessive computational circuit area overhead prevalent in contemporary multi-bit Compute-In-Memory (CIM) SRAM macro designs, this paper introduces a strategy based on 'complementary cell computation architectures coupled with complementary data inputs.' this approach establishes a high-bandwidth, high-compute-performance, and expansible multi-bit CIM SRAM macro, particularly apt for the highly parallel matrix multiplication operations encountered in neural network algorithms. the proposed CIM SRAM macro unit executes efficient binary multiplications up to 8 bits width without reliance on additional circuits such as ADC or incurring data loading delays, thereby reinforcing computational precision and system bandwidth. Simulation outcomes illustrate that, when operated at a frequency of 1GHz, the HSBC SRAM achieves an area efficiency uplift to 0. 556 MB /mm 2 and 186TOPS/mm 2 by expanding both input and weight data widths to 8 bits. © 2024 IEEE.

关键词： Static random access storage

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An FPGA-Based Top-K Gradient Compression Accelerator for Distributed Deep Learning Training 17

An FPGA-Based Top-K Gradient Compression Accelerator for Dis...

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17th IEEE international conference on Solid-State and Integrated Circuit Technology, ICSICT 2024

作者： Wang, Ziyao Zhang, Jiayu Li, Kunyue Sun, Jialei Dong, Feng Chen, Ke Qiao, Yong Jiang, Jianfei National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai China Beijing iQlYI Science & Technology Co.. Ltd.. Shanghai China

ISBN: (纸本)9798350361834

In distributed neural network training with multiple machines and devices, communication limitations often create efficiency bottlenecks due to the frequent exchange of model parameters and gradient information between computing nodes. this paper proposes an FPGA-based accelerator leveraging the gradient compression algorithm Top-K sparsification, which enhances performance by offloading computationally intensive compression operations to the FPGA. Experimental results demonstrate that the FPGA compression accelerator designed in this study achieves superior computing performance and compression efficiency compared to compression algorithms implemented on CPUs and GPUs. Specifically, the FPGA compresses the same amount of data 3.3-3.7 times faster than parallel solutions on CPUs and 1.3-1.8 times faster than on GPUs. © 2024 IEEE.

关键词： computation offloading

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Deep Learning (DL) Dense Classifier with Long Short-Term Memory Encoder Detection and Classification against Network Attacks 17

Deep Learning (DL) Dense Classifier with Long Short-Term Mem...

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17th international conference on Electronics Computer and computation, ICECCO 2023

作者： Gulbarga, Mohammad Imtiyaz Cankurt, Selcuk Shaidullaev, Nurlan Khan, A.L. Alatoo International University Computer Science Department Bishkek Kyrgyzstan Vistula University Computer Engineering Department Warsaw Poland

ISBN: (纸本)9798350339611

the increase in network attacks is a serious threat. For the purpose of supporting study in connected areas, researchers provide datasets for network assaults. Following that, other attack detection techniques based on these datasets are suggested. In this paper, we have proposed a Deep Learning (DL) model called DL Dense Classifier with Long Short-Term Memory (LSTM) which is combination of Encoder and classifier modules to identify the type of network attacks from a massive malware dataset with 12 key malware categories and 141 features. Moreover, we have evaluated 3 classifier models on the CCCS-CIC-AndMal-2020 dataset utilizingprecision, recall, F-score, and accuracy metrics. the results of the experiments show that adding an LSTM encoder module to the DL Dense Classifier model successfully improves the accuracy of DL Dense Classifier from %87 to %91 and perform same as the ensemble model of Random Forest, which is one of the best ranked algorithms in the other research. © 2023 IEEE.

关键词： Long short-term memory

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DPSPC: A Density Peak-Based Statistical Parallel Clustering Algorithm for Big Data 17th

DPSPC: A Density Peak-Based Statistical Parallel Clustering ...

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17th international conference on Knowledge Science, Engineering and Management (KSEM)

作者： Pan, Xiaohui Deng, Jinglan Yang, Hanyu Peng, Jing Yin, Jianfei Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9789819754946;9789819754953

Data parallelism is a powerful design paradigm for clustering tasks involving large datasets. However, existing solutions suffer from three problems: (i) using non-identical distribution based partitioning methods may pose the risk of data skew;(ii) frequent communication among the divided data partitions may lead to potential performance degradation;and (iii) unnecessary full computation results in significant computational overhead. In order to address these issues, we propose a density peak-based statistical parallel clustering algorithm for big data (DPSPC). Our sampling-based approach creates equal-sized data blocks with the same statistical measures of clusters, reducing data skew and eliminating inter-block communication. By sampling a subset of blocks for computation, we avoid full computation. the experimental results suggest that the NMI index of the DPSPC algorithm is generally not 10% lower than that of other distributed density peak clustering algorithms, with runtime about one-tenth and the lowest communication volume.

关键词： Statistical Parallel Distributed Clustering Density Peak Random Sample Partition Approximate computation

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Multi-stage Pricing Mechanism in Duopoly computation Markets 17th

Multi-stage Pricing Mechanism in Duopoly Computation Market...

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CCF 17th international conference on Service Science, CCF ICSS 2024

作者： Chen, Pengyang Wang, Quyuan Liu, Jiadi Wang, Ying Guo, Zhiwei Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology Chongqing Technology and Business University Chongqing400067 China College of Computer and Information Science College of Software Southwest University Chongqing400710 China

ISBN: (纸本)9789819757596

A new generation of computing paradigms has been proposed to facilitate people’s daily lives, and there is a lot of literature on resource allocation in these paradigms to enhance the user experience. It is also essential to investigate how to incentive users to participate in these computing paradigms to share their surplus resources. In this paper, we formulate multi-stage profit-maximizing optimization problems in duopoly computation market considering consumer value, provider reputation, Bandwagon Effect, and consumer heterogeneity from an economic perspective. In the pre-sale phase we propose the Demand Priority Pricing Algorithm (DPPA) algorithm to help providers find optimal pre-sale prices. For the formal sales phase, we design the Community Proportion Prediction Algorithm (CPPA) to predict key parameters and the Incremental Priority Pricing Algorithm (IPPA) to help providers choose pricing schemes. Finally, experimental results show that our model describes market laws well and our algorithms assist providers in finding profit-maximizing pricing schemes for fair and stable markets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： computational cost

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17th conference on the theory of Quantum computation, Communication and Cryptography, TQC 2022

17th Conference on the Theory of Quantum Computation, Commun...

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17th conference on the theory of Quantum computation, Communication and Cryptography, TQC 2022

ISBN: (纸本)9783959772372

the proceedings contain 12 papers. the topics discussed include: quantum algorithms for learning a hidden graph;quantum algorithm for stochastic optimal stopping problems with applications in finance;the parametrized complexity of quantum verification;averaged circuit eigenvalue sampling;classical simulation of quantum circuits with partial and graphical stabilizer decompositions;on converses to the polynomial method;the quantum approximate optimization algorithm at high depth for MaxCut on large-girth regular graphs and the Sherrington-Kirkpatrick model;a constant lower bound for any quantum protocol for secure function evaluation;approximating output probabilities of shallow quantum circuits which are geometrically-local in any fixed dimension;and memory compression with quantum random-access gates.

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An Evolutionary Game Based computation Offloading for an UAV Network in MEC 17th

An Evolutionary Game Based Computation Offloading for an UAV...

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17th international conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Gu, Qi Shen, Bo Northwestern Polytech Univ Sch Comp Sci Xian 710129 Peoples R China

ISBN: (纸本)9783031192104;9783031192111

With the rapid development of information technology, low-cost unmanned aerial vehicles (UAVs) appear. With advanced sensing and actuating technologies, they are being increasingly applied to a variety of scenarios. However, considering their limited computing resource and restricted battery capability, the computation-intensive tasks or data-intensive tasks will face tough challenges. With the aid of Mobile Edge Computing (MEC), moving computation-intensive tasks from resource-constrained UAVs to edge cloud servers can significantly save energy and finally achieve impressive *** paper proposes an evolutionary game based algorithm to solve the computation offloading problem for UAVs. By replicator dynamics, UAVs select the suitable service provider to offload the computation tasks via achieving a tradeoff between time delay, energy consumption and monetary cost when network externality exists. Simulation results show that the proposed algorithm can rapidly converge to evolutionary equilibrium and achieve desirable performance.

关键词： Evolutionary game Replicator dynamics computation offloading Unmanned aerial vehicles Mobile edge computing

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Efficient Post Quantum Random Oblivious Transfer Based on Lattice 17th

Efficient Post Quantum Random Oblivious Transfer Based on La...

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17th international conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Xu, Lidong Wang, Mingqiang Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China

ISBN: (纸本)9783031192074;9783031192081

the large scale multiparty computation and private set intersection requires a number of oblivious transfer instances as subroutines, but the implementation of oblivious transfer protocols is relatively slow. An feasible way is to use the oblivious transfer variant called random oblivious transfer. In this paper, we propose a 1-out-of-2 random oblivious transfer protocol and extend it to a 1-out-of-k random oblivious transfer protocol based on the LWE assumption, quantum computation and measurement. then, we analysis the stand-alone security of our 1-out-of-2 random oblivious transfer protocol under various malicious situations and prove its universally composable security in UC framework. As for the security of our 1-out-of-k random oblivious transfer protocol, the similar results can be obtained.

关键词： Oblivious transfer LWE problem Quantum computation UC-security

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